Nicholas Ruozzi is an Assistant Professor of Computer Science at The University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. His research focuses on machine learning, statistical inference, and probabilistic graphical models, with applications in virtual reality (VR) training, computer vision, and explainable AI. He has contributed to areas such as tractable probabilistic modeling, activity recognition in videos, and user tracking in VR systems. His work often bridges theoretical foundations with practical applications, such as developing algorithms for data privacy in VR training sessions and enhancing deep learning models through hybrid approaches with graphical models. Recent research trends include exploring multimodal interaction, distributionally robust models, and novel instance detection techniques in computer vision. Ruozzi's publications span topics like user identifiability in VR, predictive task guidance in AR, and systematic analysis of device interactions in VR systems. While no specific awards or grants are listed, his contributions reflect a strong emphasis on interdisciplinary applications of machine learning and probabilistic methods.
Ziran Wang is an Assistant Professor in the Department of Civil Engineering at Purdue University's College of Engineering, appointed as new faculty in 2022. His research bridges digital twin technologies, autonomous driving systems, and human-machine interaction to advance intelligent transportation solutions. Ph.D. in Mechanical Engineering, University of California, Riverside Prior role: Principal Researcher at Toyota North America His work focuses on creating personalized autonomous driving experiences through machine learning, emphasizing safety and efficiency in real-world applications. Key areas include multimodal large language model integration, federated learning for privacy-preserving data sharing, and cooperative perception frameworks. He develops novel approaches for digital twin-based traffic simulation, medical emergency detection in vehicles, and human behavior modeling in complex urban environments. Analysis of his 2024-2025 publications reveals a dominant trend toward generative AI applications in autonomous driving, particularly for perception-prediction-planning integration and real-world validation. His research increasingly incorporates digital twins for safety-critical testing and explores medical applications through in-vehicle health monitoring systems. Dr. Wang advises graduate students including Wenhui Huang and leads the Purdue Digital Twin Lab, which develops advanced simulation and testing platforms for autonomous systems. His lab maintains strong industry partnerships with Toyota for real-world deployment and validation of research成果.
Mannes Poel is a researcher specializing in Datamanagement & Biometrics with a focus on applied machine learning. His work spans healthcare analytics, sensor technology optimization, and meta-learning frameworks for missing data. ORCID: 0000-0002-3813-9732 Active Domains : Artificial Intelligence, Medical Predictive Modeling, Industrial Sensor Analytics Collaboration Network : Interdisciplinary work with institutions in healthcare (e.g., Diagnostics journal) and engineering (e.g., IEEE MEMS conference) Scientific Achievements : Best paper award at Intetain 2107 (2017) Research Trends : His recent publications (2024-2025) emphasize explainable AI for missing data in clinical contexts and machine learning-enhanced sensor systems for industrial fluid dynamics. These works combine traditional ML with real-time data processing and cross-domain model adaptation.
Qiaoning Carol Zhang serves as Assistant Professor of Human Systems Engineering within The Polytechnic School at Arizona State University's Ira A. Fulton Schools of Engineering. Her research investigates the critical intersection of human perception, social contexts, and emerging technologies including artificial intelligence, robotics, and automated vehicles, with emphasis on creating intuitive, user-friendly, and inclusive systems. Her academic foundation includes: Ph.D. in Information, University of Michigan (2023) M.S. in Industrial and Operations Engineering, University of Michigan (2018) B.S. in Industrial Engineering, Hunan University (2016) Dr. Zhang's research program centers on understanding how individual differences and social dynamics shape technology interactions. Key focus areas include Human-AI Collaboration , Human-Robot Interaction , Human Factors in Automated Vehicles , and User Experience Design . Her work employs interdisciplinary methodologies to ensure technology adapts to diverse user needs across complex socio-technical environments, particularly in transportation and healthcare robotics. Analysis of her 15 most recent publications (2021-2025) reveals dominant themes in trust dynamics within automated vehicles, with significant attention to explainable AI interfaces. Research consistently examines how voice characteristics (gender, similarity), explanation modalities, and individual differences (age, personality) impact cognitive and affective trust. Recent work extends to healthcare robotics for elderly populations using Kano model analysis to identify critical user requirements. No scientific awards are documented in the provided materials. Dr. Zhang actively recruits Ph.D. candidates and undergraduate/master's researchers with backgrounds in human-computer interaction, data science, and interdisciplinary fields (design, computer science, cognitive science). She emphasizes opportunities in transportation technology, healthcare robotics, AI, and UX research/design, requiring applicants to submit CVs, research statements, and representative work samples. While specific grants aren't detailed, her research scope indicates substantial funding in human factors and emerging technology domains. Her research team focuses on developing empathetic technology through projects examining trust calibration in automated vehicles and healthcare robot design for older adults. Current initiatives include voice interface optimization for diverse user groups and Kano model applications in home healthcare robotics, aiming to bridge technical capabilities with human-centered design principles.
Gerry Dozier is the Charles D. McCrary Eminent Chair Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering. His research focuses on artificial intelligence, computational intelligence, cybersecurity, identity science, and cyber identity protection. He leads initiatives like the Center for Artificial Intelligence and Cybersecurity Engineering and contributes to Alabama's AI policy through the state commission. Dr. Dozier holds a Ph.D. from North Carolina State University and has pioneered work in adversarial machine learning, biometric security, and low-resource language NLP. Education: Ph.D. Computer Science, North Carolina State University (Raleigh) M.S. Computer Science, North Carolina State University (Raleigh) B.S. Computer Science, Northeastern Illinois University Research Themes: Combines AI with cybersecurity to address modern digital challenges. Specializes in adversarial attacks/defenses, biometric authentication systems, and ethical NLP applications in multilingual contexts. Active in developing tools for sentiment analysis in underrepresented languages and mitigating biases in automated systems. Impact: Spearheaded Auburn's AI@AU initiative with lecture series and forums. Collaborates internationally on facial recognition, malware detection, and medical AI applications like bacterial vaginosis diagnosis. His work bridges theoretical CS advancements with real-world security and ethical considerations. Labs/Teams: Directs Auburn's AI & Cybersecurity Engineering Center and contributes to interdisciplinary groups like the McCrary Institute for Cyber and Critical Infrastructure Security.
Esa Rahtu is a Professor in the Department of Computer Science at Aalto University, Finland. His research focuses on computer vision, machine learning, and deep learning applications. He leads projects in image coding, neural networks, 3D reconstruction, object pose estimation, and anomaly detection. Rahtu has contributed to over 98 research outputs since 2017, with recent work emphasizing Gaussian splatting for SLAM, neural radiance fields, and hybrid video codecs for human-machine compatibility. His expertise spans visual-inertial odometry (e.g., ADVIO dataset), LiDAR-based place recognition, and manufacturing quality control systems. Key areas include: 3D scene reconstruction using Gaussian splatting techniques Deep learning models for anomaly detection in industrial processes Hybrid video codecs optimizing human perception and machine processing Multi-sensor fusion for robotic navigation and indoor mapping Notable datasets include ADVIO for visual-inertial odometry and FIORD for 3D reconstruction benchmarking. His research aligns with UN SDG 9 (Industry, Innovation & Infrastructure) and SDG 4 (Quality Education) through advancements in smart manufacturing and educational technology. Rahtu has received continuous research funding, including a grant period from April to June 2018. His work emphasizes practical applications, collaborating on real-world challenges like paper manufacturing quality control and smartphone-based 3D reconstruction.
Wei Pang is a Professor of Computer Science and Bicentennial Research Leader at the School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh. He leads the BCML Lab and is affiliated with the Edinburgh Centre for Robotics and National Robotarium. His expertise spans bio-inspired computing, machine learning, and AI applications in healthcare, robotics, and sustainability. Pang holds a PhD in Computing Science from the University of Aberdeen, with prior roles including Senior Lecturer at the University of Aberdeen and research fellowships in systems biology. Affiliations: Heriot-Watt University, Edinburgh Centre for Robotics, National Robotarium Education: PhD in Computing Science (2009), MEng (by research), BSc (Jilin University, China) Research Interests: Bio-inspired computing (e.g., artificial immune systems, swarm intelligence), machine learning (deep learning, explainable AI), healthcare applications (medical imaging, disease detection), and interdisciplinary projects in robotics and environmental science. His work addresses challenges in robust AI, fairness, and accountable machine learning. Recent Projects: EPSRC-funded RAIns and MI projects, CRUK-funded Endo.AI, and PRIME project on minority ethnic communities' digital experiences. His research has secured over £10M in grants, including £3.5M institutional funding. Awards: Scottish Crucible Award (2015), ADMA Best Paper Runner-Up (2016), EPSRC PRIME Award (2024) Grants/Advising: Supervised 12 PhD completions; contributed to £10M+ external funding. Labs/Teams: BCML Lab (focusing on bio-inspired AI), collaborations with Oxford, Cambridge, and industrial partners like Weather2 and Data2Text.
Ana Filipa Sequeira is a researcher affiliated with INESC TEC, Porto, Portugal, and the University of Porto. Her work spans biometrics, fairness in AI, and explainable artificial intelligence. She has contributed to advancements in face recognition, synthetic data applications, and bias mitigation through techniques like knowledge distillation and model compression. Institution: INESC TEC (Porto, Portugal) Research Themes: Face recognition, fairness, explainability, synthetic data, biometric security Her recent publications focus on addressing demographic biases in face recognition systems, developing privacy-preserving explainable methods, and evaluating synthetic data's impact. She collaborates extensively with researchers like Pedro C. Neto, Jaime S. Cardoso, and Naser Damer. Sequeira has participated in organizing and analyzing competitions such as FRCSyn, BIOSIG, and SYN-MAD, emphasizing robust evaluation frameworks. Her work intersects technical innovation with ethical considerations, advocating for responsible AI applications in biometrics.
Professor Clinton Fookes is a faculty member at the Queensland University of Technology (QUT) within the School of Electrical Engineering & Robotics . His research focuses on leveraging computer vision and artificial intelligence to develop automated systems that understand, anticipate, and interact with human behaviors, with applications in medical diagnostics, autonomous vehicles, defense, and industrial efficiency . Research areas include AI adaptability, multimodal biosignal analysis, and human-machine interaction Collaborates with CSIRO Data61, Defence Science and Technology Group, Orica, Airbus, and Sentient Vision Systems Develops systems for human action detection, infrastructure monitoring, and stress response prediction His work addresses critical challenges in AI deployment, such as environmental adaptability and reducing diagnostic errors in medical and autonomous systems. Recent publications highlight trends in self-supervised learning, zero-shot knowledge transfer, multimodal integration , and 3D reconstruction for healthcare , while exploring ethical AI use in sectors like mining and defense . Professor Fookes emphasizes interdisciplinary collaboration, bridging engineering, medicine, and social sciences to advance AI systems capable of real-world impact. His research agenda includes improving AI memory capabilities and explainability for safer, more reliable automation.
Assoc. Prof. Nhien An Le Khac is an Associate Professor at the School of Computer Science, University College Dublin. He serves as Programme Director for the MSc in Forensic Computing & Cybercrime Investigation, which has trained over 1,500 law enforcement officers globally. His research focuses on cybersecurity, digital forensics, AI security, and secure healthcare IT systems. He holds a PhD from Institut National Polytechnique de Grenoble (France) and has supervised 9 PhD students. His work includes pioneering contributions to electromagnetic side-channel analysis (EM-SCA) for IoT forensics, blockchain forensics, and AI-based fraud detection. Education: BSc/MSc: Vietnam National University, Ho Chi Minh City PhD: Institut National Polytechnique de Grenoble, France Professional Certificate in University Teaching & Learning: UCD Research Interests: Cybersecurity, Digital Forensics, AI Security, Machine Learning, Cloud Computing, Big Data Analytics, Healthcare IT Security. Recent Article Trends: Focus on EM-SCA for IoT device forensics, illicit Bitcoin transaction tracking, and cross-device ML portability. His work bridges theoretical AI advancements with practical forensic applications, emphasizing privacy preservation and explainable AI. Awards & Recognition: World’s Top 2% Scientists (2024) UCD Teaching Excellence Awards (2022, 2018) Best Paper Awards at Elsevier, AI-2022, and DFRWS conferences Grants & Advising: Principal Investigator on grants like Cloud Atlas, CERBERUS, and Urban ARK. Advised 9 PhD students who now work in academia/research globally. Active in funding initiatives like ML-Labs (SFI-funded). Labs & Teams: Leads ASEADOS Lab and maintains datasets like EM-SCA and InSDN. Collaborates globally on forensic frameworks and cybersecurity tools.
Dr. Richard Jiang is a Senior Lecturer (Associate Professor) at Lancaster University's School of Computing and Communications. His research focuses on Artificial Intelligence, Neurocomputing, Quantum AI, Privacy Computing, and Medical Computing. He has pioneered secure pattern recognition in encrypted domains and quantum neuromorphic computing. With over £1M in research grants from EPSRC and others, he has authored 100+ publications and supervised over 20 PhD students. Dr. Jiang's work includes the Face2Brain method for neurodegenerative assessment and explainable models for brain aging analysis. He contributes actively to academic committees, editorial boards, and conferences like the World Conference on eXplainable AI. His research spans ethical AI frameworks, quantum algorithms for medical imaging, and privacy-preserving biometric systems.
Zachi Attia, Ph.D., M.B.A., is an Associate Professor at the Mayo Clinic College of Medicine and Science, Rochester, Minnesota. His research focuses on applying artificial intelligence (AI) and machine learning to cardiac biosignals, particularly for early disease detection and prediction. He holds primary and joint appointments as Consultant in AI within the Department of Cardiovascular Medicine, collaborating with the Center for Digital Health and the Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery. Education: Ph.D. in Electrical Engineering from the University of Minnesota, Rochester; BSc and MSc in Electrical Engineering from Ben Gurion University, Israel. Dr. Attia's work centers on developing AI models that analyze multimodal cardiac data (ECGs, echocardiograms, angiograms) to detect silent diseases. His research includes pragmatic clinical trials to validate AI's impact on patient outcomes, explainable AI for biological insights, and integrating AI dashboards into medical records for clinical usability. Recent publications highlight AI applications in detecting atrial fibrillation, hypertrophic cardiomyopathy, and pulmonary hypertension via ECG analysis. Scientific Awards: No explicit awards mentioned in the text. Email: attia.itzhak@mayo.edu
Sneha Das is an Assistant Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), specializing in Speech and Language Technology, Machine Learning, and Privacy-Preserving AI. Her research bridges technical innovation with applications in mental health and physiological signal analysis. Her work focuses on Speech Emotion Recognition , Distributed Speech Processing , and Explainable AI , with recent publications exploring model interpretability, speaker anonymization, and physiological data analysis for emotion detection. She actively supervises PhD students in projects involving AI for mental health and hydroacoustic modeling of fish behavior. Key Research Areas: Speech Emotion Recognition (SER) Privacy and Fairness in Speech Processing Transfer Learning with Physiological Time Series AI Applications in Health and Aquaculture Notable achievements include earning a DSc (Tech) degree for her thesis on robust distributed speech processing. She also contributes to educational activities, including teaching applied statistics and R programming to PhD students.
Prof. Blerim Rexha is a full professor at the University of Prishtina's Faculty of Electrical and Computer Engineering, Kosovo. With a Ph.D. in Computer Engineering from Vienna University of Technology (2004), he has led research in cybersecurity, blockchain, machine learning, and electronic voting systems. His teaching portfolio includes data, computer, and internet security courses. Education : Ph.D. in Computer Engineering (Vienna), Electrical Engineer MSc (Prishtina), specialized certifications in software engineering, biometrics, and .NET programming. His research spans cybersecurity (DDoS mitigation, face authentication attacks), blockchain applications (electronic voting bridges, transaction privacy), and machine learning integration (boosted trees for intrusion detection, LSTM for vulnerability scanning). He has contributed to cloud security through novel encryption methods and AI-driven attack detection. Recent publications focus on energy efficiency in cloud vs on-premises systems, XGBoost/CatBoost/LightGBM comparisons for network security, and blockchain bridges for e-voting. His work has addressed privacy preservation in video data, SMS encryption, and eID card pseudo-profiles. Awards include the 2024 Marin Barleti Prize for academic contributions and Best Paper Awards in election security (2015) and Kosovo website vulnerabilities (2013). Honors : Marin Barleti Prize (2024) Cyber Security Ambassador (2018) ICT Academician of the Year (2016) Best Paper Awards (2015, 2013) As academic advisor to the KosovaCyberTeam , he mentors students like Korab Keqekolla and Abian Morina. His leadership extends to Kosovo's Cyber Security State Training Center curriculum development and jury roles in Albanian ICT Awards .
Pedro R. M. Inácio is an Associate Professor at the University of Beira Interior (UBI) , where he teaches information assurance, cybersecurity, and computer simulation courses in undergraduate and graduate programs. He serves as Pro-Rector for the Digital University and Data Protection Officer at UBI, and leads the Cross Cutting Skills Lab and the Network Security research group at Instituto de Telecomunicações. His work bridges academia and industry, including a PhD at Nokia Siemens Networks Portugal.
